Chapter 5 ODD Protocol
This chapter presents the ASA ABM v2 following the ODD (Overview, Design concepts, Details) protocol (Grimm et al., 2010, 2020). The ODD protocol provides a standardized way to describe agent-based models.
5.1 1. Purpose and Patterns
5.1.1 1.1 Purpose
The purpose of this model is to simulate the Attraction-Selection-Attrition (ASA) processes in organizations to understand:
- Primary Questions:
- How do organizations evolve toward homogeneity through ASA processes?
- What role do individual preferences (homophily vs. diversity) play in organizational composition?
- How do different selection strategies affect long-term organizational diversity?
- What are the dynamics of satisfaction and turnover in evolving organizations?
- Secondary Questions:
- How does interaction frequency affect organizational culture formation?
- What is the relationship between personality diversity and organizational stability?
- How do hiring cycles and growth rates influence organizational evolution?
5.1.2 1.2 Patterns
The model aims to reproduce and explain the following patterns observed in organizational research:
- Organizational Homogenization: Organizations tend to become more homogeneous over time
- Fit-Satisfaction-Retention Spiral: Better fitting employees are more satisfied and stay longer
- Diversity-Performance Relationship: Complex relationships between diversity and organizational outcomes
- Turnover Cascades: Departures can trigger additional turnover through social effects
5.2 2. Entities, State Variables, and Scales
5.2.1 2.1 Entities
The model contains three types of entities:
- Agents (Employees and Applicants)
- Represent individuals who work in or apply to the organization
- Possess personality traits, identity, and preferences
- Can transition from applicant to employee
- Organization
- Collection of agents marked as active employees
- Emergent properties calculated from constituent agents
- Interactions
- Pairwise encounters between agents
- Store history of social experiences
5.2.2 2.2 State Variables
5.2.2.1 Agent-level State Variables
Variable | Type | Range/Values | Description |
---|---|---|---|
agent_id | character | Unique string | Unique identifier |
identity_category | character | “A”-“E” (default) | Categorical identity |
openness | numeric | ~N(0,1) | Big Five personality trait |
conscientiousness | numeric | ~N(0,1) | Big Five personality trait |
extraversion | numeric | ~N(0,1) | Big Five personality trait |
agreeableness | numeric | ~N(0,1) | Big Five personality trait |
emotional_stability | numeric | ~N(0,1) | Big Five personality trait |
homophily_preference | numeric | ~N(0,1) | Preference for similarity |
diversity_preference | numeric | ~N(0,1) | Preference for diversity |
attraction | numeric | Real | Attraction to organization |
satisfaction | numeric | Real | Current satisfaction level |
tenure | integer | 0+ | Time steps in organization |
hire_date | integer | 0+ | Time step when hired |
is_active | logical | TRUE/FALSE | Currently employed |
application_time | integer | 0+ | Time as applicant (applicants only) |
5.2.2.2 Organization-level Variables (Emergent)
Variable | Type | Description |
---|---|---|
size | integer | Number of active agents |
identity_diversity | numeric | Shannon entropy of identities |
avg_satisfaction | numeric | Mean satisfaction of active agents |
personality_means | numeric vector | Mean of each Big Five trait |
personality_sds | numeric vector | SD of each Big Five trait |
5.3 3. Process Overview and Scheduling
5.3.1 3.1 Process Overview
Each time step consists of the following processes in order:
- Update Tenure: Increment tenure for all active agents
- Execute Interactions: Agents interact with randomly selected partners
- Update Satisfaction: Calculate new satisfaction based on interactions
- Execute Turnover: Remove agents below satisfaction threshold
- Hiring Cycle (periodic):
- Age applicant pool
- Recruit new applicants
- Calculate applicant attraction
- Filter applicant pool
- Select and hire new employees
- Calculate Metrics: Record organizational state
5.4 4. Design Concepts
5.4.1 4.1 Basic Principles
The model implements the ASA framework through: - Attraction: Differential attraction based on fit - Selection: Preferential hiring of fitting individuals - Attrition: Satisfaction-based turnover
5.4.2 4.2 Emergence
The following properties emerge from individual-level behaviors: - Organizational culture (personality profile) - Identity composition and diversity - Turnover patterns and rates - Social network properties (implicit in interactions)
5.4.3 4.3 Adaptation
Agents adapt through: - Satisfaction updates based on experiences - Turnover decisions based on satisfaction - No learning or trait changes (fixed personalities)
5.4.4 4.4 Objectives
Agents seek to: - Applicants: Join organizations with high attraction - Employees: Maintain satisfaction above threshold - Organization: Hire best-fitting applicants (implicit)
5.4.6 4.6 Prediction
Agents implicitly predict: - Applicants assess organizational fit before joining - Employees evaluate satisfaction trends (through threshold mechanism)
5.4.7 4.7 Sensing
Agents can sense: - Organizational identity composition (for attraction calculation) - Interaction partner characteristics (during interactions) - Own satisfaction level
5.4.8 4.8 Interaction
- Direct: Pairwise interactions between agents
- Indirect: Through organizational composition effects
- Interaction Selection: Random pairing (extensible to networks)
5.4.9 4.9 Stochasticity
Stochastic elements include: - Agent trait initialization (~N(0,1)) - Identity assignment (uniform random) - Interaction partner selection (random) - Interaction valence noise (scaled by emotional stability) - Hiring decisions (when using random selection)
5.6 6. Input Data
No external data files required. All agent characteristics generated stochastically during initialization.
5.7 7. Submodels
5.7.1 7.1 Attraction Calculation
For Applicants:
Attraction = (HomophilyPref × PropSameIdentity) +
(DiversityPref × DiversityIndex)
5.7.2 7.2 Interaction Valence
When agents i and j interact:
Valence_ij = -|Extraversion_i - Extraversion_j| +
(Conscientiousness_i - Extraversion_j) +
Agreeableness_i +
IdentityBonus +
ε
Where:
- IdentityBonus = HomophilyPref_i if same identity,
DiversityPref_i if different
- ε ~ N(0, |EmotionalStability_i|)
5.7.3 7.3 Satisfaction Update
Satisfaction_i = Attraction_i +
mean(RecentInteractionValences) +
(HomophilyPref_i × PropSameIdentity) +
(DiversityPref_i × DiversityIndex) +
EmotionalStability_i
5.7.4 7.4 Turnover Decision
Threshold Model:
if (Satisfaction < TurnoverThreshold) then Leave
Probabilistic Model:
P(Leave) = BaseTurnoverRate / (1 + exp(Satisfaction × Weight))
5.8 References
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., … & DeAngelis, D. L. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1-2), 115-126.
Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760-2768.
Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., … & Ayllón, D. (2020). The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23(2), 7.